Concepts of mental life across cultures: Supplemental analysis

In this notebook we conduct exploratory factor analyses (EFAs) on the datasets for our studies of concepts of mental life, in which each participants judged the various mental capacities of a particular target entity. We analyze datasets for adults and children from each of our five field sites: the US, Ghana, Thailand, China, and Vanuatu.

This notebook contains a supplemental analysis not presented in the main text, in which we drop participants who gave the same answer on every trial.

Adults

Samples

  country   n
       US 106
    Ghana  88
 Thailand 144
    China 120
  Vanuatu 120
    Total 578

Scale use

Factor retention: parallel analysis

Exploratory factor analysis

Factor loadings

the condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be used

Congruence

funs() is soft deprecated as of dplyr 0.8.0
Please use a list of either functions or lambdas: 

  # Simple named list: 
  list(mean = mean, median = median)

  # Auto named with `tibble::lst()`: 
  tibble::lst(mean, median)

  # Using lambdas
  list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
This warning is displayed once per session.

Bootstrapped congruence

Children

Samples

  country   n
       US 115
    Ghana 129
 Thailand 151
    China 120
  Vanuatu 128
    Total 643

Scale use

Factor retention: parallel analysis

Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefully

Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefully

Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefully

Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefully

Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.

Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.

Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.

Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.

Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefully

Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.

Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.

Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefully

Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefully

Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefully

Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.

Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefully

Parallel analysis suggests that the number of factors =  3  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.

Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.

Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefully

Parallel analysis suggests that the number of factors =  3  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefully

Parallel analysis suggests that the number of factors =  3  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefully

Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.

Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.

Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefully

Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefully

Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefully

Parallel analysis suggests that the number of factors =  4  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.

Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefully

Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefully

Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.

Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefully

Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefully

Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefully

Parallel analysis suggests that the number of factors =  3  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.

Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefully

Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.

Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefully

Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.

Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefully

Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.

Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.

Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.

Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefully

Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.

Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefully

Parallel analysis suggests that the number of factors =  3  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.

Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.

Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.

Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.

Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.

Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.

Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.

Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 

Exploratory factor analysis

Factor loadings

the condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be used

Congruence

See All samples, below.

Bootstrapped congruence

All samples

Congruence

the condition has length > 1 and only the first element will be usedVectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.the condition has length > 1 and only the first element will be usedVectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.the condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be used

Developmental comparisons

Column `country` joining character vector and factor, coercing into character vector

Column `country` joining character vector and factor, coercing into character vector

Column `country` joining character vector and factor, coercing into character vector

Column `country` joining character vector and factor, coercing into character vector

Column `country` joining character vector and factor, coercing into character vector

Column `country` joining character vector and factor, coercing into character vector